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Snow Density Retrieval in Quebec Using Space-Borne SMOS Observations
by
Shi, Jiancheng
, Gao, Xiaowen
, Yang, Jianwei
, Husi, Letu
, Zhao, Tianjie
, Pan, Jinmei
, Jiang, Lingmei
, Bai, Yu
, Peng, Zhiqing
in
Artificial satellites in remote sensing
/ Bias
/ Comparative analysis
/ Data assimilation
/ Data collection
/ Datasets
/ Density
/ Distribution
/ Environmental aspects
/ Feasibility studies
/ Forests
/ Frozen ground
/ frozen soils
/ Grain size
/ Mathematical models
/ Measurement
/ microwave radiometers
/ Moisture effects
/ multiple-angle
/ Parameters
/ passive microwave remote sensing
/ Quebec
/ Radiative transfer
/ Radiometers
/ Remote sensing
/ Root-mean-square errors
/ roughness
/ salinity
/ Satellite observation
/ SMOS
/ Snow
/ Snow density
/ Snow depth
/ Snow-water equivalent
/ Snowpack
/ Soil moisture
/ Soil Moisture and Ocean Salinity satellite
/ soil water
/ Temperature
/ temporal variation
/ Temporal variations
/ Water depth
2023
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Snow Density Retrieval in Quebec Using Space-Borne SMOS Observations
by
Shi, Jiancheng
, Gao, Xiaowen
, Yang, Jianwei
, Husi, Letu
, Zhao, Tianjie
, Pan, Jinmei
, Jiang, Lingmei
, Bai, Yu
, Peng, Zhiqing
in
Artificial satellites in remote sensing
/ Bias
/ Comparative analysis
/ Data assimilation
/ Data collection
/ Datasets
/ Density
/ Distribution
/ Environmental aspects
/ Feasibility studies
/ Forests
/ Frozen ground
/ frozen soils
/ Grain size
/ Mathematical models
/ Measurement
/ microwave radiometers
/ Moisture effects
/ multiple-angle
/ Parameters
/ passive microwave remote sensing
/ Quebec
/ Radiative transfer
/ Radiometers
/ Remote sensing
/ Root-mean-square errors
/ roughness
/ salinity
/ Satellite observation
/ SMOS
/ Snow
/ Snow density
/ Snow depth
/ Snow-water equivalent
/ Snowpack
/ Soil moisture
/ Soil Moisture and Ocean Salinity satellite
/ soil water
/ Temperature
/ temporal variation
/ Temporal variations
/ Water depth
2023
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Snow Density Retrieval in Quebec Using Space-Borne SMOS Observations
by
Shi, Jiancheng
, Gao, Xiaowen
, Yang, Jianwei
, Husi, Letu
, Zhao, Tianjie
, Pan, Jinmei
, Jiang, Lingmei
, Bai, Yu
, Peng, Zhiqing
in
Artificial satellites in remote sensing
/ Bias
/ Comparative analysis
/ Data assimilation
/ Data collection
/ Datasets
/ Density
/ Distribution
/ Environmental aspects
/ Feasibility studies
/ Forests
/ Frozen ground
/ frozen soils
/ Grain size
/ Mathematical models
/ Measurement
/ microwave radiometers
/ Moisture effects
/ multiple-angle
/ Parameters
/ passive microwave remote sensing
/ Quebec
/ Radiative transfer
/ Radiometers
/ Remote sensing
/ Root-mean-square errors
/ roughness
/ salinity
/ Satellite observation
/ SMOS
/ Snow
/ Snow density
/ Snow depth
/ Snow-water equivalent
/ Snowpack
/ Soil moisture
/ Soil Moisture and Ocean Salinity satellite
/ soil water
/ Temperature
/ temporal variation
/ Temporal variations
/ Water depth
2023
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Snow Density Retrieval in Quebec Using Space-Borne SMOS Observations
Journal Article
Snow Density Retrieval in Quebec Using Space-Borne SMOS Observations
2023
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Overview
Snow density varies spatially, temporally, and vertically within the snowpack and is the key to converting snow depth to snow water equivalent. While previous studies have demonstrated the feasibility of retrieving snow density using a multiple-angle L-band radiometer in theory and in ground-based radiometer experiments, this technique has not yet been applied to satellites. In this study, the snow density was retrieved using the Soil Moisture Ocean Salinity (SMOS) satellite radiometer observations at 43 stations in Quebec, Canada. We used a one-layer snow radiative transfer model and added a τ-ω vegetation model over the snow to consider the forest influence. We developed an objective method to estimate the forest parameters (τ, ω) and soil roughness (SD) from SMOS measurements during the snow-free period and applied them to estimate snow density. Prior knowledge of soil permittivity was used in the entire process, which was calculated from the Global Land Data Assimilation System (GLDAS) soil simulations using a frozen soil dielectric model. Results showed that the retrieved snow density had an overall root-mean-squared error (RMSE) of 83 kg/m3 for all stations, with a mean bias of 9.4 kg/m3. The RMSE can be further reduced if an artificial tuning of three predetermined parameters (τ, ω, and SD) is allowed to reduce systematic biases at some stations. The remote sensing retrieved snow density outperforms the reanalysis snow density from GLDAS in terms of bias and temporal variation characteristics.
Publisher
MDPI AG
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